
The paper presents a suitable technique to establish semantic similarities among the set of keywords of two different Move Commons initiatives. The results show that the ideas behind the proposal can be used for the goal of the paper. The study of more complex similarity definition, such as considering the number of high related keywords, can enhance the accuracy of the technique. 

We consider that although the results are promising, they can be significantly improved with some of the ideas presented in related work.

In Section \ref{sec:w1} we have seen that the accuracy of the concept assignation to keywords is an step where information is lost. In order to improve this part of our algorithm we can use the technique presented in \cite{eps267792} to disambiguate keywords. The use of spell checkers and machine translation can increase the number of classified keywords by dealing with keywords written in other languages or misspelled.

To obtain more accurate results we should test our tool with available benchmarks such as \cite{Finkelstein:2002:WordSimilarity-353} and consider other configurations for our experiments (e.g. changing the parameters such as the number of initiatives or similar initiatives returned or studying other semantic similarity measures). Since our tool uses Move Commons initiatives and Move Commons provide semantic information of the initiatives, the evaluation of how considering this information affects to the similarity will be also an interesting issue.



